Dataset is coming from INSEE. INSEE is the official french institute gathering data of many types around France. It can be demographic (Births, Deaths, Population Density…), Economic (Salary, Firms by activity / size…) and more. Data and descriptin can be found at this link. Four files are in the dataset:
baseetablissementpartrancheeffectif : give information on the number of firms in every french town, categorized by size , come from INSEE.
namegeographicinformation : give geographic data on french town (mainly latitude and longitude, but also region / department codes and names )
netsalarypertownper_category : salaries around french town per job categories, age and sex
population : demographic information in France per town, age, sex and living mode
departments.geojson : contains the borders of french departments. From Gregoire David (github)
This report is using only netsalarypertownper_category and name_geographic_information parts. Other files are described for further analysis purposes if needed. These datasets can be merged by : CODGEO = code_insee.
| unique code of the town | name of the town | mean net salary | mean net salary per hour for executive | mean net salary per hour for middle manager | mean net salary per hour for employee | mean net salary per hour for worker | mean net salary for women | mean net salary per hour for feminin executive | mean net salary per hour for feminin middle manager | ... | mean net salary per hour for masculin worker | mean net salary per hour for 18-25 years old | mean net salary per hour for 26-50 years old | mean net salary per hour for >50 years old | mean net salary per hour for women between 18-25 years old | mean net salary per hour for women between 26-50 years old | mean net salary per hour for women >50 years old | mean net salary per hour for men between 18-25 years old | mean net salary per hour for men between 26-50 years old | mean net salary per hour for men >50 years old | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 01004 | Ambérieu-en-Bugey | 13.7 | 24.2 | 15.5 | 10.3 | 11.2 | 11.6 | 19.1 | 13.2 | ... | 11.6 | 10.5 | 13.7 | 16.1 | 9.7 | 11.8 | 12.5 | 11.0 | 14.9 | 18.6 |
| 1 | 01007 | Ambronay | 13.5 | 22.1 | 14.7 | 10.7 | 11.4 | 11.9 | 19.0 | 13.3 | ... | 11.7 | 9.8 | 13.8 | 14.6 | 9.2 | 12.2 | 12.5 | 10.2 | 14.9 | 16.4 |
| 2 | 01014 | Arbent | 13.5 | 27.6 | 15.6 | 11.1 | 11.1 | 10.9 | 19.5 | 11.7 | ... | 11.8 | 9.3 | 13.3 | 16.0 | 8.9 | 10.6 | 12.5 | 9.6 | 15.1 | 18.6 |
| 3 | 01024 | Attignat | 12.9 | 21.8 | 14.1 | 11.0 | 11.3 | 11.4 | 19.0 | 13.0 | ... | 11.6 | 9.6 | 12.9 | 14.2 | 9.3 | 11.4 | 12.2 | 9.7 | 13.8 | 15.9 |
| 4 | 01025 | Bâgé-la-Ville | 13.0 | 22.8 | 14.1 | 10.5 | 11.1 | 11.6 | 19.4 | 13.6 | ... | 11.4 | 9.4 | 12.8 | 15.2 | 9.0 | 11.8 | 12.3 | 9.7 | 13.4 | 16.9 |
| 5 | 01027 | Balan | 13.9 | 22.2 | 15.1 | 11.0 | 11.4 | 12.5 | 20.3 | 14.0 | ... | 11.7 | 9.7 | 14.1 | 15.4 | 9.5 | 12.8 | 13.0 | 9.9 | 15.3 | 17.2 |
| 6 | 01031 | Bellignat | 12.4 | 24.0 | 13.1 | 10.5 | 10.4 | 10.9 | 20.7 | 11.8 | ... | 10.8 | 9.3 | 12.5 | 13.3 | 8.9 | 11.0 | 11.5 | 9.6 | 13.3 | 14.9 |
| 7 | 01032 | Béligneux | 14.0 | 23.1 | 15.3 | 10.9 | 11.3 | 12.4 | 20.5 | 13.9 | ... | 11.6 | 9.7 | 13.9 | 16.7 | 9.7 | 12.4 | 13.8 | 9.6 | 15.0 | 19.3 |
| 8 | 01033 | Bellegarde-sur-Valserine | 11.5 | 21.2 | 13.5 | 9.9 | 10.5 | 10.3 | 20.8 | 12.3 | ... | 11.0 | 9.6 | 11.5 | 12.7 | 9.2 | 10.3 | 11.3 | 10.0 | 12.3 | 13.9 |
| 9 | 01034 | Belley | 12.4 | 23.4 | 14.1 | 10.3 | 10.5 | 11.0 | 21.5 | 13.0 | ... | 10.9 | 9.7 | 12.3 | 13.7 | 9.3 | 11.2 | 11.4 | 9.9 | 13.0 | 15.4 |
10 rows × 26 columns
<class 'pandas.core.frame.DataFrame'> RangeIndex: 5136 entries, 0 to 5135 Data columns (total 26 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 unique code of the town 5136 non-null object 1 name of the town 5136 non-null object 2 mean net salary 5136 non-null float64 3 mean net salary per hour for executive 5136 non-null float64 4 mean net salary per hour for middle manager 5136 non-null float64 5 mean net salary per hour for employee 5136 non-null float64 6 mean net salary per hour for worker 5136 non-null float64 7 mean net salary for women 5136 non-null float64 8 mean net salary per hour for feminin executive 5136 non-null float64 9 mean net salary per hour for feminin middle manager 5136 non-null float64 10 mean net salary per hour for feminin employee 5136 non-null float64 11 mean net salary per hour for feminin worker 5136 non-null float64 12 mean net salary for man 5136 non-null float64 13 mean net salary per hour for masculin executive 5136 non-null float64 14 mean net salary per hour for masculin middle manager 5136 non-null float64 15 mean net salary per hour for masculin employee 5136 non-null float64 16 mean net salary per hour for masculin worker 5136 non-null float64 17 mean net salary per hour for 18-25 years old 5136 non-null float64 18 mean net salary per hour for 26-50 years old 5136 non-null float64 19 mean net salary per hour for >50 years old 5136 non-null float64 20 mean net salary per hour for women between 18-25 years old 5136 non-null float64 21 mean net salary per hour for women between 26-50 years old 5136 non-null float64 22 mean net salary per hour for women >50 years old 5136 non-null float64 23 mean net salary per hour for men between 18-25 years old 5136 non-null float64 24 mean net salary per hour for men between 26-50 years old 5136 non-null float64 25 mean net salary per hour for men >50 years old 5136 non-null float64 dtypes: float64(24), object(2) memory usage: 1.0+ MB
| mean net salary | mean net salary per hour for executive | mean net salary per hour for middle manager | mean net salary per hour for employee | mean net salary per hour for worker | mean net salary for women | mean net salary per hour for feminin executive | mean net salary per hour for feminin middle manager | mean net salary per hour for feminin employee | mean net salary per hour for feminin worker | ... | mean net salary per hour for masculin worker | mean net salary per hour for 18-25 years old | mean net salary per hour for 26-50 years old | mean net salary per hour for >50 years old | mean net salary per hour for women between 18-25 years old | mean net salary per hour for women between 26-50 years old | mean net salary per hour for women >50 years old | mean net salary per hour for men between 18-25 years old | mean net salary per hour for men between 26-50 years old | mean net salary per hour for men >50 years old | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 5136.000000 | 5136.000000 | 5136.000000 | 5136.000000 | 5136.000000 | 5136.000000 | 5136.000000 | 5136.000000 | 5136.000000 | 5136.000000 | ... | 5136.000000 | 5136.000000 | 5136.000000 | 5136.00000 | 5136.000000 | 5136.000000 | 5136.000000 | 5136.000000 | 5136.000000 | 5136.000000 |
| mean | 13.706386 | 23.703836 | 14.575876 | 10.564505 | 11.235650 | 12.038026 | 20.220502 | 13.274260 | 10.308879 | 9.827161 | ... | 11.498189 | 9.549766 | 13.495444 | 15.87722 | 9.161565 | 12.055160 | 13.174143 | 9.820872 | 14.485981 | 17.679809 |
| std | 2.559329 | 2.836183 | 1.490110 | 0.811775 | 1.221755 | 1.787995 | 2.327550 | 0.990166 | 0.747563 | 1.104684 | ... | 1.289598 | 0.997444 | 2.363144 | 3.58586 | 0.453023 | 1.825306 | 2.249391 | 1.539949 | 2.852490 | 4.530257 |
| min | 10.200000 | 16.000000 | 11.600000 | 8.700000 | 8.300000 | 9.300000 | 12.000000 | 10.600000 | 8.700000 | 6.100000 | ... | 8.900000 | 7.900000 | 9.700000 | 10.50000 | 7.500000 | 9.100000 | 9.500000 | 7.800000 | 9.600000 | 10.800000 |
| 25% | 12.100000 | 21.900000 | 13.800000 | 10.000000 | 10.600000 | 10.900000 | 18.800000 | 12.600000 | 9.800000 | 9.200000 | ... | 10.800000 | 9.200000 | 12.000000 | 13.70000 | 8.900000 | 10.900000 | 11.700000 | 9.400000 | 12.700000 | 14.900000 |
| 50% | 13.000000 | 23.200000 | 14.400000 | 10.400000 | 11.000000 | 11.500000 | 20.000000 | 13.100000 | 10.100000 | 9.700000 | ... | 11.300000 | 9.500000 | 12.900000 | 15.00000 | 9.100000 | 11.600000 | 12.600000 | 9.700000 | 13.800000 | 16.600000 |
| 75% | 14.400000 | 24.900000 | 15.100000 | 10.900000 | 11.600000 | 12.700000 | 21.400000 | 13.800000 | 10.600000 | 10.200000 | ... | 11.900000 | 9.700000 | 14.300000 | 16.90000 | 9.400000 | 12.700000 | 14.000000 | 10.000000 | 15.500000 | 19.000000 |
| max | 43.300000 | 51.500000 | 54.600000 | 17.500000 | 46.300000 | 26.700000 | 35.500000 | 19.000000 | 16.100000 | 28.100000 | ... | 53.200000 | 60.600000 | 38.100000 | 56.90000 | 12.000000 | 26.600000 | 31.000000 | 93.300000 | 45.400000 | 68.600000 |
8 rows × 24 columns
<class 'pandas.core.frame.DataFrame'> RangeIndex: 36840 entries, 0 to 36839 Data columns (total 14 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 EU_circo 36840 non-null object 1 code_région 36840 non-null int64 2 nom_région 36840 non-null object 3 chef.lieu_région 36840 non-null object 4 numéro_département 36840 non-null object 5 nom_département 36840 non-null object 6 préfecture 36840 non-null object 7 numéro_circonscription 36840 non-null int64 8 nom_commune 36840 non-null object 9 codes_postaux 36840 non-null object 10 code_insee 36840 non-null int64 11 latitude 33911 non-null float64 12 longitude 33999 non-null object 13 éloignement 33878 non-null float64 dtypes: float64(2), int64(3), object(9) memory usage: 3.9+ MB
In Q1 2021 the unemployment rate in France has reached 8.1%.
The average net salary per hour for the whole country currently achieved: 13.71€. For womens it is 12.04€, and for mens 14.85€
Net payment is starting to grow with the age. The discrepancies between gender payments are getting more visible with the age.
Some of France Regions have been merged during last period. Data have been updated to reflect those changes and new Region names.
| code_insee | EU_circo | code_région | nom_région | chef.lieu_région | |
|---|---|---|---|---|---|
| 0 | 01024 | Sud-Est | 82 | Rhône-Alpes | Lyon |
| 1 | 01029 | Sud-Est | 82 | Rhône-Alpes | Lyon |
| 2 | 01038 | Sud-Est | 82 | Rhône-Alpes | Lyon |
| 3 | 01040 | Sud-Est | 82 | Rhône-Alpes | Lyon |
| 4 | 01245 | Sud-Est | 82 | Rhône-Alpes | Lyon |
| ... | ... | ... | ... | ... | ... |
| 36835 | 97613 | Outre-Mer | 5 | Mayotte | Mamoudzou |
| 36836 | 97614 | Outre-Mer | 5 | Mayotte | Mamoudzou |
| 36837 | 97615 | Outre-Mer | 5 | Mayotte | Mamoudzou |
| 36838 | 97616 | Outre-Mer | 5 | Mayotte | Mamoudzou |
| 36839 | 97617 | Outre-Mer | 5 | Mayotte | Mamoudzou |
36693 rows × 5 columns
In the whole country there are only two towns where is noted higher average payment for women than men. However, the difference is slight.
[NbConvertApp] Converting notebook Report_net_salary_france.ipynb to html [NbConvertApp] Writing 6433030 bytes to Report_net_salary_france.html